Neural network approach to forecast the state of the Internet of Things elements

Kotenko, Igor; Saenko, Igor; Skorik, Fadey; Bushuev, Sergey Valentinovich

doi:10.1109/scm.2015.7190434

Cited by 35 publications

(15 citation statements)

References 9 publications

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“…This section highlights discusses, compares, summarizes and critiques more than eighty research articles on artificial neural network model's application to the diverse area of the economy. The comparison was made based on (i) author(s)/year of publication (ii) ANN modeling (iii) ANN area of application (iii) studied contribution to human challenges (with references to Supplementary Table 2) [ 11 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 , 158 , 159 , 160 , 161 , 162 , 163 , 164 , 165 , 166 , 167 , 168 , 169 , 170 , 171 , 172 , 173 , 174 , 175 , 176 , 177 , 178 , …”

Section: Main Textmentioning

confidence: 99%

State-of-the-art in artificial neural network applications: A survey

Abiodun

Omolara

Dada

et al. 2018

Heliyon

2,160

785

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This is a survey of neural network applications in the real-world scenario. It provides a taxonomy of artificial neural networks (ANNs) and furnish the reader with knowledge of current and emerging trends in ANN applications research and area of focus for researchers. Additionally, the study presents ANN application challenges, contributions, compare performances and critiques methods. The study covers many applications of ANN techniques in various disciplines which include computing, science, engineering, medicine, environmental, agriculture, mining, technology, climate, business, arts, and nanotechnology, etc. The study assesses ANN contributions, compare performances and critiques methods. The study found that neural-network models such as feedforward and feedback propagation artificial neural networks are performing better in its application to human problems. Therefore, we proposed feedforward and feedback propagation ANN models for research focus based on data analysis factors like accuracy, processing speed, latency, fault tolerance, volume, scalability, convergence, and performance. Moreover, we recommend that instead of applying a single method, future research can focus on combining ANN models into one network-wide application.

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Section: Main Textmentioning

confidence: 99%

State-of-the-art in artificial neural network applications: A survey

Abiodun

Omolara

Dada

et al. 2018

Heliyon

2,160

785

View full text Add to dashboard Cite

show abstract

“…Significant results with deep neural networks have led them to be the most commonly used classifiers in machine learning [125,57]. [126] present the method to forecast the states of IoT elements based on an artificial neural network. The presented architecture of the neural network is a combination of a multilayered perceptron and a probabilistic neural network.…”

Section: Feed Forward Neural Networkmentioning

confidence: 99%

Machine learning for internet of things data analysis: a survey

Mahdavinejad

Rezvan

Barekatain

et al. 2018

Digital Communications and Networks

895

378

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Rapid developments in hardware, software, and communication technologies have allowed the emergence of Internet-connected sensory devices that provide observation and data measurement from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25-50 billion. As the numbers grow and technologies become more mature, the volume of data published will increase. Internet-connected devices technology, referred to as Internet of Things (IoT), continues to extend the current Internet by providing connectivity and interaction between the physical and cyber worlds. In addition to increased volume, the IoT generates Big Data characterized by velocity in terms of time and location dependency, with a

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“…Smart traffic scenarios are probably the most studied application [36] [37]. Smart health [38] and Smart cities [39] are also common, together with prediction solutions [17] (typically about weather). Sparse works on AI and IoT for smart agriculture [40] [43] or traffic air control [5] have been also reported.…”

Section: State Of the Artmentioning

confidence: 99%

A Predictor-Corrector Algorithm Based on Laurent Series for Biological Signals in the Internet of Medical Things

Bordel

You

2020

IEEE Access

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In future engineered systems for medical applications, a tight real-time integration between physical and computational processes will be required. That integration is achieved using feedback control loops which need high quality input data streams. However, hardware platforms can barely provide such high-quality data sequences (especially if mobile nodes are considered), and mechanisms to improve and polish physical and biological signals are then necessary. This paper proposes a predictor-corrector algorithm to improve the quality and precision of data (biological) signals in Internet of Medical Things deployments, especially if composed of mobile nodes. The proposed algorithm employs an Artificial Intelligence approach and statistical learning techniques to predict future data samples and correct errors in received information. Employed mathematical models follow a prediction-correction scheme and are based on complex functions, Laurent series and the idea of complex envelope. Simulation techniques are used to evaluate the performance of the proposed solution, showing that it improves the precision of traditional linear interpolation techniques up to 85%, and cubic splines up to 20%. Processing delay during operation is, for the referred precision, around 200ms.

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Neural network approach to forecast the state of the Internet of Things elements

Cited by 35 publications

References 9 publications

State-of-the-art in artificial neural network applications: A survey

State-of-the-art in artificial neural network applications: A survey

Machine learning for internet of things data analysis: a survey

A Predictor-Corrector Algorithm Based on Laurent Series for Biological Signals in the Internet of Medical Things

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